1.1 Подготовка и анализ изначальных данных¶
Импортирование библиотек¶
In [ ]:
# для визуализации
import matplotlib.pyplot as plt
%matplotlib inline
# для работы с файлами
import os
# для более удобных словарей
from collections import defaultdict
# проверка версий
print('matplotlib version: 3.10.0')
Анализ данных¶
назначаем пути которые нам нужны
для удобства, я создал двумерный список для оперирования разными датасетами
In [125]:
def get_path(part: str = 'test', data: str = 'images') -> str:
'''функция для получения путя к данным'''
return fr'D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\{part}\{data}'
# части данных
dp = [
['test', 'images', 'labels', 'labels_rw'],
['train', 'images', 'labels', 'labels_rw'],
['valid', 'images', 'labels', 'labels_rw'],
['start_labels', 'labels_test', 'labels_train', 'labels_valid']
]
In [126]:
# тестирование функции get_path()
print(get_path())
print(get_path(dp[0][0], dp[0][1]))
# успешно
D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\test\images D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\test\images
изучаем распределение классов в данных
In [ ]:
colors = ['#0f1d87', '#0a64f7', '#6abee6'] # назначаю цвета
fig, axes = plt.subplots(1,3, figsize=(15,5)) # инициализирую кол-во графиков и общий размер
fig.suptitle('Class distribution in folders')
for i in range(3): # прохожу по каждому датасету списком
label_dir = get_path(dp[3][0], dp[3][i+1])
class_stats = defaultdict(int)
for label_file in os.listdir(label_dir):
with open(os.path.join(label_dir, label_file), 'r') as f:
lines = f.readlines()
for line in lines:
class_id = int(line.split()[0])
class_stats[class_id] += 1
print(f'Class statistics in the folder {dp[i][0]}:') # вывожу статистику по классам
for class_id, count in class_stats.items():
print(f'Class {class_id}: {count} objects')
print('-----------------------------------------')
axes[i].bar(class_stats.keys(), class_stats.values(), color=colors[i]) # добавляю часть графика
axes[i].set_title(f'Folder {dp[i][0]}') # название датасета
axes[i].set_xlabel('Class ID') # подпись x
axes[i].set_ylabel('Number of input') # подпись y
axes[i].set_xticks(list(class_stats.keys()))
plt.tight_layout()
plt.show()
после исследования классов в YAT, я могу сказать что:
- 0 - название продукта |
- 1 - цена без скидки |
- 2 - актуальная цена | <=== я должен научить модель находить на фото эти классы
- 3 - вид скидки
у меня нет надобности увеличивать размеры классов (я так думаю)
Для того чтобы модель лучше научилась определять цеу продукта, удалю все остальные классы в разметке, оставляя только актуальную цену
In [ ]:
start_lbl = ['labels_test', 'labels_train', 'labels_valid'] # названия папок со старыми разметками
for i in range(3):
label_dir = fr'D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\start_labels\{start_lbl[i]}' # используя f строку инициализирую полный путь к папке
label_rw_dir = get_path(dp[i][0], dp[1][2]) # путь к обновленному файлу
processed = 0
for filename in os.listdir(label_dir): # прохожусь по каждому файлу
filepath = os.path.join(label_dir, filename)
new_filepath = os.path.join(label_rw_dir, filename) # создаю новый путь файла
with open(filepath, 'r') as f:
lines = f.readlines()
filtered_lines = []
for line in lines: # прохожусь по строчкам в файле
parts = line.strip().split() # сплитую строчку по пробелам
if parts and parts[0] == '2': # если строка есть, и начинается с '2' то:
parts[0] = '0' # заменяю 2 на 0
updated_line = ' '.join(parts) + '\n' # собираю строку заново
filtered_lines.append(updated_line) # собираю файл заново
with open(new_filepath, 'w') as f: # сохраняю новый файл с разметкой по 1 классу с ценой
f.writelines(filtered_lines)
processed+=1
print(f'Processing complete for {start_lbl[i]}.')
print(f'Updated markups are saved in the {label_rw_dir} folder.')
print(f'Processed files: {processed}')
print('-----------------------------------------')
Используя YAT, я доразметил и переразметил данные, корректируя и дополняя их
данные изначально были разделены на тестовый, тренировочный и валидационный датасеты, и их я буду использовать для обучения модели
1.2 Подбор алгоритма обучения¶
Архитектура модели¶
YOLOv8n (3.2M)
- task=detect
- mode=train
- model=yolov8n.pt
- data=D:\Helper\MLBazyak\homework\06_01\06_01_hw\data.yaml
- epochs=3
- time=None
- patience=100
- batch=8
- imgsz=640
- save=True
- save_period=-1
- cache=False
- device=None
- workers=8
- project=None
- name=price_detection_v3
- exist_ok=False
- pretrained=True
- optimizer=auto
- verbose=True
- seed=0
- deterministic=True
- single_cls=False
- rect=False
- cos_lr=True
- close_mosaic=10
- resume=False
- amp=True
- fraction=1.0
- profile=False
- freeze=None
- multi_scale=False
- overlap_mask=True
- mask_ratio=4
- dropout=0.0
- val=True
- split=val
- save_json=False
- save_hybrid=False
- conf=None
- iou=0.7
- max_det=300
- half=False
- dnn=False
- plots=True
- source=None
- vid_stride=1
- stream_buffer=False
- visualize=False
- augment=False
- agnostic_nms=False
- classes=None
- retina_masks=False
- embed=None
- show=False
- save_frames=False
- save_txt=False
- save_conf=False
- save_crop=False
- show_labels=True
- show_conf=True
- show_boxes=True
- line_width=None
- format=torchscript
- keras=False
- optimize=False
- int8=False
- dynamic=False
- simplify=True
- opset=None
- workspace=None
- nms=False
- lr0=0.01
- lrf=0.01
- momentum=0.937
- weight_decay=0.0005
- warmup_epochs=3.0
- warmup_momentum=0.8
- warmup_bias_lr=0.1
- box=7.5
- cls=0.5
- dfl=1.5
- pose=12.0
- kobj=1.0
- nbs=64
- hsv_h=0.015
- hsv_s=0.7
- hsv_v=0.4
- degrees=0.0
- translate=0.1
- scale=0.5
- shear=0.0
- perspective=0.0
- flipud=0.0
- fliplr=0.5
- bgr=0.0
- mosaic=1.0
- mixup=0.0
- copy_paste=0.0
- copy_paste_mode=flip
- auto_augment=randaugment
- erasing=0.4
- crop_fraction=1.0
- cfg=None
- tracker=botsort.yaml
- save_dir=runs\detect\price_detection_v3
Я буду использовать эту версию YOLO, тк она хорошо справляется с задачами детекции, а также не сильно требовательная к ресурсам
Гиперпараметры, которые я настроил:
data = r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\data.yaml'| путь к yaml файлуepochs=3| количество эпохimgsz=640| размер изображений в данныхbatch=8| количество батчейcos_lr=True| косинусный планировщик кривой скорости обученииlr0=0.01| скорость обучения
1.3 Импорт данных для обучения нейронной сети¶
Импортирование библиотек¶
In [11]:
# импортируем модуль с YOLO
from ultralytics import YOLO
# библиотека для работы с изображениями
from PIL import Image
# версии библиотек (указаны в requirements.txt файле)
print('ultralytics version:', '8.3.58')
print('PIL version:', '11.1.0')
ultralytics version: 8.3.58 PIL version: 11.1.0
я загрузил данные в следующую структуру:
- --->
data/ - ------>
test/ - --------->
images/ - --------->
labels/ - ------>
train/ - --------->
images/ - --------->
labels/ - ------>
valid/ - --------->
images/ - --------->
labels/
1.4 Обучение нейронной сети¶
Тесты¶
тестирование YOLO модели
In [2]:
model = YOLO('yolov8n.pt') # инициализируем модель YOLOv8 nano
In [ ]:
# тестовое обучение
results_test = model.train(
data = r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\data.yaml', # путь к yaml файлу
epochs=3, # количество эпох
imgsz=640, # размер изображений в данных
batch=8, # количество батчей
cos_lr=True, # косинусный планировщик кривой скорости обучении
lr0=0.01, # скорость обучения
name='price_detection_v3' # название эксперимента
)
In [ ]:
# инициализация тестовых результатов
result_test = model(r'D:\Helper\MLBazyak\homework\06_01\price_detection\data\test\images\original_five_33_v3_jpg.rf.9e8bde5a93c18446991a3e0f37ef0c76.jpg')
for result in result_test:
img = result.plot()
img = Image.fromarray(img)
img.show()
Для 3 эпох, вполне себе достойный результат:
In [ ]:
# валидация модели
model.val()
Обучение модели¶
In [4]:
model = YOLO(r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\runs\detect\price_detection_v3\weights\best.pt') # загружаю модель
In [6]:
# инициализация финальных результатов
results = model.train(
data = r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\data.yaml',
epochs=10,
imgsz=640,
batch=8,
cos_lr=True,
lr0=0.01,
name='price_detection_v4')
Ultralytics 8.3.61 Python-3.11.9 torch-2.5.1+cpu CPU (11th Gen Intel Core(TM) i5-1135G7 2.40GHz)
engine\trainer: task=detect, mode=train, model=D:\Helper\MLBazyak\homework\06_01\06_01_hw\runs\detect\price_detection_v3\weights\best.pt, data=D:\Helper\MLBazyak\homework\06_01\06_01_hw\data.yaml, epochs=10, time=None, patience=100, batch=8, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=price_detection_v42, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=True, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs\detect\price_detection_v42
from n params module arguments
0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
22 [15, 18, 21] 1 751507 ultralytics.nn.modules.head.Detect [1, [64, 128, 256]]
Model summary: 225 layers, 3,011,043 parameters, 3,011,027 gradients, 8.2 GFLOPs
Transferred 355/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
train: Scanning D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\train\labels.cache... 914 images, 0 backgrounds, 0 corrupt: 100%|██████████| 914/914 [00:00<?, ?it/s] val: Scanning D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\valid\labels.cache... 261 images, 0 backgrounds, 0 corrupt: 100%|██████████| 261/261 [00:00<?, ?it/s]
Plotting labels to runs\detect\price_detection_v42\labels.jpg...
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: AdamW(lr=0.002, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0) Image sizes 640 train, 640 val Using 0 dataloader workers Logging results to runs\detect\price_detection_v42 Starting training for 10 epochs... Closing dataloader mosaic Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/10 0G 1.29 1.055 1.261 2 640: 100%|██████████| 115/115 [12:00<00:00, 6.27s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 17/17 [00:55<00:00, 3.26s/it]
all 261 308 0.976 0.935 0.985 0.55
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 0G 1.446 1.069 1.376 3 640: 100%|██████████| 115/115 [12:12<00:00, 6.37s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 17/17 [01:08<00:00, 4.02s/it]
all 261 308 0.929 0.935 0.982 0.569
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 0G 1.413 0.9695 1.334 2 640: 100%|██████████| 115/115 [12:27<00:00, 6.50s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 17/17 [00:58<00:00, 3.42s/it]
all 261 308 0.902 0.922 0.963 0.521
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 0G 1.397 0.869 1.341 3 640: 100%|██████████| 115/115 [10:38<00:00, 5.55s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 17/17 [00:57<00:00, 3.41s/it]
all 261 308 0.974 0.982 0.991 0.588
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 0G 1.385 0.8031 1.341 2 640: 100%|██████████| 115/115 [10:46<00:00, 5.62s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 17/17 [00:57<00:00, 3.39s/it]
all 261 308 0.975 0.977 0.993 0.574
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 0G 1.343 0.7087 1.318 3 640: 100%|██████████| 115/115 [09:16<00:00, 4.84s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 17/17 [00:39<00:00, 2.31s/it]
all 261 308 0.952 0.987 0.99 0.602
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 0G 1.334 0.6755 1.306 2 640: 100%|██████████| 115/115 [08:00<00:00, 4.18s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 17/17 [00:38<00:00, 2.27s/it]
all 261 308 0.981 0.987 0.994 0.617
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 0G 1.304 0.6428 1.273 2 640: 100%|██████████| 115/115 [08:00<00:00, 4.18s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 17/17 [00:38<00:00, 2.29s/it]
all 261 308 0.98 0.99 0.993 0.612
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 0G 1.275 0.6027 1.259 2 640: 100%|██████████| 115/115 [08:00<00:00, 4.18s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 17/17 [00:38<00:00, 2.26s/it]
all 261 308 0.977 0.994 0.993 0.642
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 0G 1.273 0.5916 1.257 2 640: 100%|██████████| 115/115 [08:00<00:00, 4.18s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 17/17 [00:38<00:00, 2.29s/it]
all 261 308 0.981 0.99 0.994 0.643
10 epochs completed in 1.796 hours. Optimizer stripped from runs\detect\price_detection_v42\weights\last.pt, 6.2MB Optimizer stripped from runs\detect\price_detection_v42\weights\best.pt, 6.2MB Validating runs\detect\price_detection_v42\weights\best.pt... Ultralytics 8.3.61 Python-3.11.9 torch-2.5.1+cpu CPU (11th Gen Intel Core(TM) i5-1135G7 2.40GHz) Model summary (fused): 168 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 17/17 [00:32<00:00, 1.92s/it]
all 261 308 0.981 0.99 0.994 0.642
Speed: 1.8ms preprocess, 109.2ms inference, 0.0ms loss, 0.7ms postprocess per image
Results saved to runs\detect\price_detection_v42
несколько тренировочных батчей
In [7]:
model.val()
Ultralytics 8.3.61 Python-3.11.9 torch-2.5.1+cpu CPU (11th Gen Intel Core(TM) i5-1135G7 2.40GHz) Model summary (fused): 168 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs
val: Scanning D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\valid\labels.cache... 261 images, 0 backgrounds, 0 corrupt: 100%|██████████| 261/261 [00:00<?, ?it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 33/33 [01:02<00:00, 1.89s/it]
all 261 308 0.981 0.99 0.994 0.642
Speed: 4.7ms preprocess, 189.6ms inference, 0.0ms loss, 1.5ms postprocess per image
Results saved to runs\detect\price_detection_v422
Out[7]:
ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([0])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x000001BAD7950B90>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
curves_results: [[array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,
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fitness: np.float64(0.6773448912867279)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.64216])
names: {0: 'price'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.9806932022771747), 'metrics/recall(B)': np.float64(0.9895192811376727), 'metrics/mAP50(B)': np.float64(0.9940392811349407), 'metrics/mAP50-95(B)': np.float64(0.6421566257480376), 'fitness': np.float64(0.6773448912867279)}
save_dir: WindowsPath('runs/detect/price_detection_v422')
speed: {'preprocess': 4.699068507928958, 'inference': 189.56299120438965, 'loss': 0.0, 'postprocess': 1.4932950337727864}
task: 'detect'
Проверка¶
In [127]:
photos = [r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\test\images\original_five_237_v4_jpg.rf.ea11992f01fc8a1a9d3e8fa1891c6f98.jpg',
r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\test2.jpg',
r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\test\images\original_five_1381_v4_jpg.rf.f83985d20e2111de4666d99b99083109.jpg',
r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\test\images\original_magnit_179_v4_jpg.rf.d83b2ab287821a7b6f6f1c8e1927696e.jpg',
r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\test1.png']
for photo in photos:
result_fin = model(photo)
for result in result_fin:
img = result.plot()
img = Image.fromarray(img)
img.show()
image 1/1 D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\test\images\original_five_237_v4_jpg.rf.ea11992f01fc8a1a9d3e8fa1891c6f98.jpg: 480x640 1 price, 78.8ms Speed: 3.0ms preprocess, 78.8ms inference, 0.7ms postprocess per image at shape (1, 3, 480, 640) image 1/1 D:\Helper\MLBazyak\homework\06_01\06_01_hw\test2.jpg: 640x576 1 price, 134.2ms Speed: 3.6ms preprocess, 134.2ms inference, 0.5ms postprocess per image at shape (1, 3, 640, 576) image 1/1 D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\test\images\original_five_1381_v4_jpg.rf.f83985d20e2111de4666d99b99083109.jpg: 480x640 4 prices, 84.9ms Speed: 2.0ms preprocess, 84.9ms inference, 1.0ms postprocess per image at shape (1, 3, 480, 640) image 1/1 D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\test\images\original_magnit_179_v4_jpg.rf.d83b2ab287821a7b6f6f1c8e1927696e.jpg: 480x640 1 price, 156.1ms Speed: 2.2ms preprocess, 156.1ms inference, 1.2ms postprocess per image at shape (1, 3, 480, 640) image 1/1 D:\Helper\MLBazyak\homework\06_01\06_01_hw\test1.png: 448x640 2 prices, 4394.3ms Speed: 8.0ms preprocess, 4394.3ms inference, 4.0ms postprocess per image at shape (1, 3, 448, 640)
я сделал несколько фотографий ценников в местной Пятерочке, для проверки на них модели (фото 3 & 5)
OCR¶
Я использовал Tesseract и EasyOCR не так давно для пет проекта, и если
я могу доверять информации из Интернета, EasyOCR быстрый и более новый OCR инструмент
Импортирование библиотек¶
In [10]:
import easyocr
print(f'EasyOCR version: {easyocr.__version__}')
EasyOCR version: 1.7.2
In [ ]:
ocr = easyocr.Reader(['en']) # инициализирую OCR модель (english немного лучше распознает цифры)
Объединяю 2 модели¶
Импортирование библиотек¶
In [9]:
# библиотека для работы с фото
import cv2
# библиотека с регулярными выражениями
import re
print('opencv-python version:', cv2.__version__)
opencv-python version: 4.10.0
Создание функций¶
Для завершения этого модуля, я думаю нужно:
- найти bb's для картинки
- обрезать картинку по bb's
- в обрезанной картинке распознать текст используя OCR модель
может быть я буду использовать рег. выражения для структурирования вывода
пути
In [15]:
model = YOLO(r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\Helps\runs\detect\price_detection_v42\weights\best.pt') # загружаю свою модель
img = r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\test2.jpg' # путь к фотографии
In [ ]:
model.val()
In [19]:
def rec_price(det_model: YOLO = model,
ocr: easyocr.Reader = ocr,
img_dir: str = img):
'''
Процедура для обнаружения и распознавания цен на изображении.
Args:
- det_model (YOLO): Модель YOLO для обнаружения bounding box'ов цен.
- ocr (easyocr.Reader): Модель OCR для распознавания текста.
- img_dir (str): Путь к изображению, на котором нужно найти цену.
Returns:
list: Функция отображает изображение с обнаруженными ценами и возвращает список с определенными ценами
'''
image = cv2.imread(img_dir)
res = det_model(image)
image_ocr = cv2.imread(img_dir, cv2.IMREAD_GRAYSCALE)
_, binary_image = cv2.threshold(image_ocr, 100, 255, cv2.THRESH_BINARY)
image_ocr = cv2.equalizeHist(binary_image)
prices = []
for result in res:
boxes = result.boxes.xyxy.cpu().numpy()
for box in boxes:
x1, y1, x2, y2 = map(int, box)
correct = (x2-x1)*0.28
print(correct)
x2 = int(x2-correct)
crop = image_ocr[y1:y2, x1:x2]
# --------------------------------------------
# plt.figure(figsize=(5, 5))
# plt.imshow(crop, cmap='gray')
# plt.title("Cropped Image") # отображение обрезанной части картинки
# plt.axis('off')
# plt.show()
# --------------------------------------------
ocr_res = ocr.readtext(crop, allowlist='0123456789')
price = None
for detection in ocr_res:
price = detection[1]
confidence = detection[2]
match = re.search(r'\d+[\.,]?\d*', price)
if match:
price = match.group()
print(f'Price: {price}\nConfidence: {confidence:.2f}')
break
if price is not None:
cv2.rectangle(image, (x1,y1), (x2+int(correct),y2), (255, 97, 0), 2) # выделение изначального бокса
cv2.rectangle(image, (x1,y1), (x2,y2), (97, 255, 0), 2) # выделение инпута в ocr
cv2.putText(image, price + 'rub', (x1,y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 97, 0), 2)
prices.append(price)
img_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
pil_img = Image.fromarray(img_rgb)
pil_img.show()
return prices
Тестирование моей функции
(Я специально оставил фотографии, где все работает не идеально, чтобы вы могли увидеть, над чем можно поработать)
In [128]:
photos = [r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\test\images\original_five_237_v4_jpg.rf.ea11992f01fc8a1a9d3e8fa1891c6f98.jpg',
r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\test\images\original_magnit_982_v4_jpg.rf.da3cb4317478a837da79b6a3f7512d0a.jpg',
r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\test\images\original_magnit_65_v4_jpg.rf.756fc91f6651425b06f55aaa81510428.jpg',
r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\test2.jpg',
r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\test\images\original_five_1381_v4_jpg.rf.f83985d20e2111de4666d99b99083109.jpg',
r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\data\test\images\original_magnit_179_v4_jpg.rf.d83b2ab287821a7b6f6f1c8e1927696e.jpg',
r'D:\Helper\MLBazyak\homework\06_01\06_01_hw\test1.png']
for photo in photos:
rec_price(img_dir=photo)
print('--------------------------------')
0: 480x640 1 price, 92.8ms Speed: 4.7ms preprocess, 92.8ms inference, 1.0ms postprocess per image at shape (1, 3, 480, 640) 94.36000000000001 Price: 449 Confidence: 1.00 -------------------------------- 0: 480x640 2 prices, 84.1ms Speed: 2.7ms preprocess, 84.1ms inference, 2.4ms postprocess per image at shape (1, 3, 480, 640) 38.92 Price: 64 Confidence: 1.00 37.800000000000004 -------------------------------- 0: 480x640 4 prices, 68.4ms Speed: 1.0ms preprocess, 68.4ms inference, 1.0ms postprocess per image at shape (1, 3, 480, 640) 17.360000000000003 13.440000000000001 16.520000000000003 17.080000000000002 -------------------------------- 0: 640x576 1 price, 1102.7ms Speed: 28.5ms preprocess, 1102.7ms inference, 28.6ms postprocess per image at shape (1, 3, 640, 576) 37.52 Price: 89 Confidence: 1.00 -------------------------------- 0: 480x640 4 prices, 130.2ms Speed: 2.5ms preprocess, 130.2ms inference, 0.9ms postprocess per image at shape (1, 3, 480, 640) 28.000000000000004 30.240000000000002 29.400000000000002 10.360000000000001 -------------------------------- 0: 480x640 1 price, 461.5ms Speed: 5.5ms preprocess, 461.5ms inference, 2.0ms postprocess per image at shape (1, 3, 480, 640) 34.440000000000005 Price: 329 Confidence: 0.90 -------------------------------- 0: 448x640 2 prices, 570.9ms Speed: 11.6ms preprocess, 570.9ms inference, 1.0ms postprocess per image at shape (1, 3, 448, 640) 71.96000000000001 49.56 --------------------------------
Результаты:
